One-sentence Summary

Researchers from New York University and Meta AI challenge the prevailing notion of Artificial General Intelligence, arguing that AI should prioritize specialization and superhuman performance, and propose Superhuman Adaptable Intelligence (SAI), a framework for intelligence that learns to surpass humans in critical tasks and fill human skill gaps, to guide future AI development.

Key Contributions

• Proposes Superhuman Adaptable Intelligence (SAI), defined as the capacity to rapidly adapt to important tasks inside and outside the human domain, learn to exceed human performance, and fill skill gaps where humans are incapable, replacing the human-centric notion of AGI.
• Reframes progress measurement around the speed and efficiency of skill acquisition under realistic resource constraints, shifting evaluation from static human benchmarks to measurable adaptation dynamics.
• Shows that embracing SAI counters the homogenization of autoregressive models by promoting architectural diversity and specialization, and identifies self-supervised learning, predictive world models, and modular composition as promising routes to fast, reliable competence.

Introduction

The authors argue that the widely used notion of Artificial General Intelligence (AGI) is deeply ambiguous, with conflicting definitions that fuel polarized debate and conflate generality with a human-centric skill set. This ambiguity misdirects research by treating human intelligence as a universal benchmark, even though human cognition is itself a collection of specialized adaptations shaped by evolutionary constraints. As a result, progress is often measured against static, human-level task checklists rather than an agent’s ability to acquire new competence rapidly under realistic resource limits. To address this, the authors propose Superhuman Adaptable Intelligence (SAI), a guiding concept that shifts focus from an ill-defined "generality" to measurable adaptation speed and efficiency for tasks both inside and outside the human domain, explicitly embracing specialization, self-supervised learning, world models, and architectural diversity.

Experiment

The evaluation examines why existing AGI definitions fall short against three core criteria: feasibility, internal consistency, and assessability. Definitions claiming true generality violate feasibility due to the No Free Lunch theorem, while those centered on human-like generality are internally inconsistent because human intelligence is only a narrow subset of possible intelligence. Performance-oriented definitions further lack clear progress metrics, whereas learning- or adaptation-focused definitions naturally provide measurable evaluation through speed of adaptation. Overall, the analysis argues that imprecise semantics around "generality" risk misleading the field toward overly narrow goals and obscure practical paths to realization.